1. Field of the Invention
The invention relates to multispectral imaging systems that acquire images using at least two spectral bands simultaneously. The imaging systems 10 and 50 of the present invention are used, for example, for real-time detection of the contamination on agricultural commodities. The systems include an optical system comprising digital cameras, beamsplitters, lenses, and optical filters.
2. Description of the Related Art
To provide the means to ensure that the food supply is safe for consumers is the most critical mission for the food industry. Despite advances in food production and processing, there is still a need for technologies that can improve food safety. The USDA Economic Research Service estimated that microbial pathogens in food cause 6.5 to 33 million cases of human illness and up to 9,000 deaths in the U.S. each year, and cost approximately $5.6 to 9.6 billion annually in medical costs, hospitalizations, and lost work time (Busby et al., Bacterial foodborne disease: Medical costs and productivity losses. Agricultural Economics Report NO. (AER741). Washington D.C.: USDA Economic Research Service, 1996). Among these estimated costs, meat and poultry sources account for $4.5 to $7.5 billion. Potential contamination of meat and poultry sources can occur at the processing plant level when feces or ingesta are inadvertently deposited on the surface of the carcass. In order to prevent such contamination, the USDA Food Safety Inspection Service (FSIS) implemented a zero-tolerance policy that prohibits poultry carcasses from having any visible fecal contamination prior to entering the ice-water chiller tank (USDA, Enhanced poultry inspection, Proposed rule. Federal Register 59: 35659, Washington, D.C.; GPO 1994). This regulation, which is part of the FSIS Hazard Analysis Critical Control Point (HACCP) system, was designed to prevent bacterial cross-contamination among carcasses in the chiller tanks (USDA, Pathogen reduction, hazard analysis, and critical control point (HACCP) systems, final rule. Federal Register, 61:28805-38855. Washington, D.C.: GPO. 1996). With the implementation of HACCP, the food industry is mandated to establish science-based process controls, and to establish performance standards for these controls. Identification and separation of contaminated carcasses are critical to protecting the consumer from potential sources of food poisoning. At the same time, FSIS also modernized their inspection system (Busby, Food Review, Volume 20(1), 1997). However, the new program still relies on periodic human visual inspection to detect fecal contamination, which is labor intensive and prone to human error and variability. In addition, there has been a dramatic increase in water usage in most plants as a result of the zero-tolerance standard (Jones, Poultry, Volume 6, 38-41, 1999). Automated detection of fecal contaminants on raw meat, poultry, and other foodstuffs has been studied for a long time.
With poultry, for example, in a modern poultry processing plant, carcasses are placed on shackles of a processing line conveyor system for dressing and inspection. Typically, such conveyors operate at speeds of up to 140 carcasses per minute, with a six inch separation between shackles holding carcasses. Even with multiple inspectors continuously performing such inspection, as little as two seconds are allotted for the inspection of each carcass.
During this inspection period, the inspector is required to check for evidence of eight different diseases as well as for certain quality characteristics, to verify that the chicken was alive when placed on the production line, and to check for evidence of ingesta or fecal contamination. Moreover, during a typical business day operating in two eight hour shifts, a productive poultry processing plant may produce as many as 250,000 processed chickens.
After slaughter, each carcass is examined for disease or evidence of contamination that would render all or part of the carcass unfit for human consumption. Currently, the meat processing industry relies upon a variety of methods for the inspection of animal carcasses. These methods typically include human visual inspection, microbiological culture analysis, bioluminescent ATP-based assays, and antibody-based microbiological tests. Unfortunately, these procedures are labor intensive, time consuming, and do not meet the needs of the meat processing industry for an accurate high speed, non-invasive method that is amenable to inspection and real-time analysis.
A fluorescent technique can be used to detect feces from cow, deer, or swine by taking advantage of the presence of chlorophyll, which exhibits strong fluorescence emissions in the red regions of the spectrum, in the diets of those animals (Kim et al., Journal of Food Protection, Volume 66(7), 1198-1207, 2003).
Spectral sensing has been widely utilized for detecting foodborne contaminants. Techniques such as multispectral imaging, in which two to about ten different spectral bands image are obtained, and hyperspectral imaging, where up to several hundred contiguous spectral bands are measure, have been used for contaminant detection for poultry carcasses (Park et al., J. Food process Eng., Volume 27(5), 311-327, 2004; Heitschmidt et al., Trans. ASABE, Volume 50(4), 1427-1432, 2007). Researchers at the USDA Agricultural Research Service (ARS) have conducted spectroscopic analysis on poultry carcasses contaminated by feces and found that a ratio of the specific spectral bands (565 nm/517 nm) provides a good indication of the presence of fecal and ingesta contaminants on poultry carcasses (Windham et al., 2003).
Kim et al. (Journal of Food Engineering, Volume 71(1), 85-91, 2005) developed a transportable imaging system that detects fecal contamination of apples based on multispectral fluorescence image fusion. However, because the poultry diets do not contain as much chlorophylls as the diets of other animals, it is very difficult to utilize fluorescent techniques for poultry fecal detection.
Efforts have been made to develop automated or semiautomated visual inspection systems for detecting the presence of contaminants on food products during processing. Most systems utilize a technique in which the food item is irradiated with light having a frequency, for example, in the UV range, such that it causes the emission of fluorescent light radiation upon striking fecal matter or ingesta. Fluorescent light emanating from the target food item is then measured and compared with a threshold value. If the light gathered exceeds the threshold, a signal indicative of the presence of fecal contamination or ingesta is generated. Such a system is disclosed for example in U.S. Pat. Nos. 5,621,215 and 5,895,921 to Waldroup et al., and U.S. Pat. No. 5,821,546 to Xiao et al.
U.S. Pat. No. 5,914,247 to Casey et al. discloses a fecal and ingesta contamination detection system which is based on the premise that the emission of fluorescent light having a wavelength between about 660 and 680 nm is indicative of the presence of ingesta or fecal material. Thus, carcasses being processed are illuminated with UV or visible light (suitable wavelengths being between 300 and 600 nm) and the illuminated surface is then examined for the emission of fluorescent light in the 660 and 680 nm range. In a preferred embodiment, the intensity of such fluorescence in the 660-680 nm range is compared with that in the 600-620 nm range as a baseline in order to distinguish fluorescent light emissions of the carcasses themselves.
Visible and near-infrared reflectance (Vis/NIR) spectroscopy is a technique that can be used to detect contamination on foodstuffs. It is a nonconsumptive, instrumental method for fast, accurate, and precise evaluation of the chemical composition of agricultural materials (Williams, Commercial near-infrared reflectance analyzers. In Williams and Norris, eds., Near Infrared Technology in the Agricultural and Food Industries, Am. Assoc. Cereal Chem., St. Paul, Minn., 1987, pp. 107-142,). The use of Vis/NIR spectroscopic techniques for classifying wholesome, septicemic, and cadaver carcasses have been reported by Chen and Massic (ASAE, Volume 36(3), 863-889, 1993) and Chen et al., (Appl. Spectrosc., Volume 50, 910-916, 1996b). These studies were conducted with a near-infrared reflectance (NIR) probe in contact with a stationary carcass. Chen and Hruschka (ASAE Paper No. 983047, American Society of Agricultural Engineers, St. Joseph, Mich., 1999) disclosed an on-line transportable Vis/NIR system (400 to 1700 nm) in which the probe was not in contact with the carcass and carcasses were moving at rates of either 60 or 90 birds per minute. Carcasses were classified as wholesome or unwholesome with an average accuracy of 94% and 97.5% when measured in room light and in the dark, respectively. On-line trials were conducted in a slaughter establishment where spectra of normal and abnormal carcasses were measured. The Vis/NIR system measured carcasses at a rate of 70 birds per minute and was able to classify the carcasses from the spectral data with a success rate of 95% (Chen and Hruschka, 1998, supra). The Vis/NIR method showed promise for separation of wholesome and unwholesome carcasses in a partially automated system. The use of the technique to detect fecal and ingesta surface contaminants on poultry carcasses has not been attempted in the processing plant.
Machine vision is a technology for automating production processes with vision capabilities. Even though machine vision has evolved into a promising technology for many agricultural product applications, such as grading or inspection there are many factors to be considered in on-line applications; processing speed, reliability, and applicability for industrial environments (Sakar and Wolfe, Trans. ASAE, Volume 28(3), 970-979, 1985; Miller and Delwiche, Trans, ASAE, Volume 32(4), 1484-1490, 1989; Tao et al., Trans, ASAE, Volume 38(5), 1555-1561, 1995; Steinmez et al., Trans. ASAE, Volume 37(4), 1347-1353, 1994; Ni et al., ASAE Paper No. 933032, American Society of Agricultural Engineers, St. Joseph, Mich., 1993; Daley et al., Proc. SPIE, Volume 2345, 403-411, 1994). Image processing techniques have made machine vision research possible to identify and classify agricultural commodities in the spatial domain (Guyer et al., Trans. ADAE, Volume 29(6), 863-869, 1986) as well as in the spectral domain (Meyer et al., Applied Engineering in Agriculture, Volume 8(5), 715-722, 1992).
Machine vision techniques are feasible for grading and parts identification in poultry production (Daley et al., Proceedings of Robotics and Vision '88, Society of Manufacturing Engineers, Dearborn, Mich., 1988). Techniques for recognizing global or systemic defects on poultry carcasses with a color imaging system were reported by Daley et al. (1994, supra) and Chin et al., (Experimental evaluation of neural networks for inspection of chickens, Research Report of Georgia Tech. Research Institute, 1993). However, this approach had 90% accuracy for global defect classification and only 60% accuracy for local defect classification (Algorithms and techniques, RIA International Robots and Vision Conf., 1991). Even though a color imaging system has the ability to extract the salient image features, this system was not successful for totally automated inspection because of low accuracy (Daley, Color machine vision for industrial inspection advances and potential for the future, Research Report of Georgia Tech. Research Institute, 1992).
Multispectral imaging technology has potential for food inspection application. Since biological materials at different conditions have different spectral reflectance characteristics, the status of materials could be identified based on their spectral images by selecting optimum wavelengths. Several spectral image processing algorithms have been developed to differentiate wholesome carcasses from unwholesome carcasses (Park and Chen, ASAE Paper No. 946027, American Society of Agricultural Engineers, St. Joseph, Mich., 1994a; Park et al., Trans. ASAE, Volume 39(5), 1933-1941, 1996a). Use of intensities, recorded in different spectral bands of a multispectral camera for segmentation, was effective for classification of poultry carcasses (Park and Chen, Trans. ASAE, Volume 37(6), 1983-1988, 1994b; Park et al., 1996a, supra). Multispectral imaging was used for detecting unwholesome conditions, such as septicemia, cadaver, bruise, tumor, air-sacculitis, and ascites, in poultry carcasses (Park et al., 1996a, supra). Park and Chen (1994b, supra) developed a prototype multispectral imaging system for detecting abnormal poultry carcasses, specifically to determine the optimal wavelengths of multispectral filters for discerning septicemic and cadaver carcasses from normal carcasses, and to develop a discriminate function for separation of the abnormal carcasses with an accuracy of 93% for normal, 83% for septicemic, and 97% for cadaver carcasses.
Textural feature analysis of multispectral images has potential to discriminate wholesome carcasses from septicemic and cadaver carcasses with high classification accuracy of about 94% (Park and Chen, Trans. ASAE, Volume 39(4), 1485-1491, 1996). However, texture feature analysis would not be useful for an on-line system because of heavy computing time. To achieve real-time processing and analyzing of multispectral gray-scale images for on-line separation of septicemic, cadaver, tumorous, bruised, and other damaged carcasses from the wholesome carcasses, a neural network algorithm was found to be useful (Park et al., ASAE Paper No. 983070, American Society of Agricultural Engineers, St. Joseph, Mich., 1998b). Thus, image texture analysis is an important process in scene analysis because it partitions an image into meaningful regions. Lumia et al., (Pattern Recognition, Volume 16(1), 39-46, 1983) described a method for discriminating texture classes based on the measurements of small regions determined by an initial segmentation of the image for categorizing homogeneous regions. Park and Chen (1996, supra) have reported that textural feature analysis of multispectral images containing Vis/NIR wavelengths based on co-occurrence matrices was feasible for discriminating abnormal from normal poultry carcasses at 542 nm.
Development of high speed and reliable inspection systems to ensure safe production of poultry processing has become an important issue. Two dual-wavelength vision systems were developed for on-line machine vision inspection of poultry carcasses (Chao et al., ASAE Paper No. 993118, American Society of Agricultural Engineers, St. Joseph, Mich., 1999). A real-time multispectral image processing algorithm was developed from neural network models with different learning rules and transfer functions for on-line poultry carcass inspection (Park et al., Journal of Agricultural Engineering Research, Volume 69, 351-363, 1998c). The classification accuracy with dual-wavelength spectral images was much higher than single wavelength spectral images in identifying unwholesome poultry carcasses (Chao et al., 1999, supra). Object oriented software was developed for on-line capture, off-line development of classification models, and on-line prediction of wholesome and unwholesome carcasses.
An extension of multispectral imaging is known as hyperspectral imaging which is also referred to as imaging spectrometry. Whereas multispectral imaging consists of measurements from two to about ten discrete wavelengths for a given image, hyperspectral imaging measures more than 10 contiguous wavelengths, often many more. Like multispectral imaging, hyperspectral imaging is an imaging technique that combines aspects of conventional imaging with spectrometry and radiometry. The result is a technique that is capable of providing an absolute radiometric measurement over a contiguous spectral range for each and every pixel of an image. Thus, data from a hyperspectral image contains two-dimensional spatial information plus spectral information over the spectral image. These data can be considered as a three dimensional hypercube which can provide physical and geometric observations of size dimension, orientation, shape, color, and texture, as well as chemical/molecular information such as water, fat, proteins, and other hydrogen-bonded constituent as described above in other Vis/NIR research. Hyperspectral imaging is often used in remote sensing applications (Schowengerdt, The nature of remote sensing, In: Remote Sensing: Models and Methods for Image Processing, San Diego, Academic Press, 1997, pp 1-33), but is also being utilized in medical, biological, agricultural, and industrial areas as well (Lu and Chen, SPIE, Volume 3544, 121-133, 1998; Heitschmdit et al., SPIE, Volume 3544, 134-137, 1998; Levenson et al., SPIE, Volume 3438, 300-312, 1998; Lu et al., ASAE Paper No. 993120, American Society of Agricultural Engineers, St. Joseph, Mich., 1999; Willoughby et al., SPIE, Volume 2599, 264-272, 1996).
While various systems have been developed for detecting contaminants on food, there still remains a need in the art for a more effective and portable system for detecting contaminants, especially fecal contaminants on poultry carcasses used for human consumption. The present invention, different from prior art systems, provides systems which are a portable multispectral imaging systems as well as a contaminant detection algorithm.